{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch Normalization\n",
    "One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train. \n",
    "One idea along these lines is batch normalization which was proposed by [3] in 2015.\n",
    "\n",
    "The idea is relatively straightforward. Machine learning methods tend to work better when their input data consists of uncorrelated features with zero mean and unit variance. When training a neural network, we can preprocess the data before feeding it to the network to explicitly decorrelate its features; this will ensure that the first layer of the network sees data that follows a nice distribution. However, even if we preprocess the input data, the activations at deeper layers of the network will likely no longer be decorrelated and will no longer have zero mean or unit variance since they are output from earlier layers in the network. Even worse, during the training process the distribution of features at each layer of the network will shift as the weights of each layer are updated.\n",
    "\n",
    "The authors of [3] hypothesize that the shifting distribution of features inside deep neural networks may make training deep networks more difficult. To overcome this problem, [3] proposes to insert batch normalization layers into the network. At training time, a batch normalization layer uses a minibatch of data to estimate the mean and standard deviation of each feature. These estimated means and standard deviations are then used to center and normalize the features of the minibatch. A running average of these means and standard deviations is kept during training, and at test time these running averages are used to center and normalize features.\n",
    "\n",
    "It is possible that this normalization strategy could reduce the representational power of the network, since it may sometimes be optimal for certain layers to have features that are not zero-mean or unit variance. To this end, the batch normalization layer includes learnable shift and scale parameters for each feature dimension.\n",
    "\n",
    "[3] [Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n",
    "Internal Covariate Shift\", ICML 2015.](https://arxiv.org/abs/1502.03167)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "run the following from the cs231n directory and try again:\n",
      "python setup.py build_ext --inplace\n",
      "You may also need to restart your iPython kernel\n"
     ]
    }
   ],
   "source": [
    "# As usual, a bit of setup\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "    \"\"\" returns relative error \"\"\"\n",
    "    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))\n",
    "\n",
    "def print_mean_std(x,axis=0):\n",
    "    print('  means: ', x.mean(axis=axis))\n",
    "    print('  stds:  ', x.std(axis=axis))\n",
    "    print() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train:  (49000, 3, 32, 32)\n",
      "y_train:  (49000,)\n",
      "X_val:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_test:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.items():\n",
    "  print('%s: ' % k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: forward\n",
    "In the file `cs231n/layers.py`, implement the batch normalization forward pass in the function `batchnorm_forward`. Once you have done so, run the following to test your implementation.\n",
    "\n",
    "Referencing the paper linked to above would be helpful!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before batch normalization:\n",
      "  means:  [ -2.3814598  -13.18038246   1.91780462]\n",
      "  stds:   [27.18502186 34.21455511 37.68611762]\n",
      "\n",
      "After batch normalization (gamma=1, beta=0)\n",
      "  means:  [5.32907052e-17 7.04991621e-17 4.22578639e-17]\n",
      "  stds:   [0.99999999 1.         1.        ]\n",
      "\n",
      "After batch normalization (gamma= [1. 2. 3.] , beta= [11. 12. 13.] )\n",
      "  means:  [11. 12. 13.]\n",
      "  stds:   [0.99999999 1.99999999 2.99999999]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after batch normalization   \n",
    "\n",
    "# Simulate the forward pass for a two-layer network\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print('Before batch normalization:')\n",
    "print_mean_std(a,axis=0)\n",
    "\n",
    "gamma = np.ones((D3,))\n",
    "beta = np.zeros((D3,))\n",
    "# Means should be close to zero and stds close to one\n",
    "print('After batch normalization (gamma=1, beta=0)')\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=0)\n",
    "\n",
    "gamma = np.asarray([1.0, 2.0, 3.0])\n",
    "beta = np.asarray([11.0, 12.0, 13.0])\n",
    "# Now means should be close to beta and stds close to gamma\n",
    "print('After batch normalization (gamma=', gamma, ', beta=', beta, ')')\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After batch normalization (test-time):\n",
      "  means:  [-0.03927354 -0.04349152 -0.10452688]\n",
      "  stds:   [1.01531428 1.01238373 0.97819988]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "\n",
    "for t in range(50):\n",
    "  X = np.random.randn(N, D1)\n",
    "  a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "  batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "bn_param['mode'] = 'test'\n",
    "X = np.random.randn(N, D1)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print('After batch normalization (test-time):')\n",
    "print_mean_std(a_norm,axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: backward\n",
    "Now implement the backward pass for batch normalization in the function `batchnorm_backward`.\n",
    "\n",
    "To derive the backward pass you should write out the computation graph for batch normalization and backprop through each of the intermediate nodes. Some intermediates may have multiple outgoing branches; make sure to sum gradients across these branches in the backward pass.\n",
    "\n",
    "Once you have finished, run the following to numerically check your backward pass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  1.7029235612572515e-09\n",
      "dgamma error:  7.420414216247087e-13\n",
      "dbeta error:  2.8795057655839487e-12\n"
     ]
    }
   ],
   "source": [
    "# Gradient check batchnorm backward pass\n",
    "np.random.seed(231)\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: batchnorm_forward(x, a, beta, bn_param)[0]\n",
    "fb = lambda b: batchnorm_forward(x, gamma, b, bn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n",
    "\n",
    "_, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = batchnorm_backward(dout, cache)\n",
    "#You should expect to see relative errors between 1e-13 and 1e-8\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: alternative backward\n",
    "In class we talked about two different implementations for the sigmoid backward pass. One strategy is to write out a computation graph composed of simple operations and backprop through all intermediate values. Another strategy is to work out the derivatives on paper. For example, you can derive a very simple formula for the sigmoid function's backward pass by simplifying gradients on paper.\n",
    "\n",
    "Surprisingly, it turns out that you can do a similar simplification for the batch normalization backward pass too.  \n",
    "Given a set of inputs $X=\\begin{bmatrix}x_1\\\\x_2\\\\...\\\\x_N\\end{bmatrix}$, \n",
    "we first calculate the mean $\\mu=\\frac{1}{N}\\sum_{k=1}^N x_k$ and variance $v=\\frac{1}{N}\\sum_{k=1}^N (x_k-\\mu)^2.$    \n",
    "With $\\mu$ and $v$ calculated, we can calculate the standard deviation $\\sigma=\\sqrt{v+\\epsilon}$  and normalized data $Y$ with $y_i=\\frac{x_i-\\mu}{\\sigma}.$\n",
    "\n",
    "\n",
    "The meat of our problem is to get $\\frac{\\partial L}{\\partial X}$ from the upstream gradient $\\frac{\\partial L}{\\partial Y}.$ It might be challenging to directly reason about the gradients over $X$ and $Y$ - try reasoning about it in terms of $x_i$ and $y_i$ first.\n",
    "\n",
    "You will need to come up with the derivations for $\\frac{\\partial L}{\\partial x_i}$, by relying on the Chain Rule to first calculate the intermediate $\\frac{\\partial \\mu}{\\partial x_i}, \\frac{\\partial v}{\\partial x_i}, \\frac{\\partial \\sigma}{\\partial x_i},$ then assemble these pieces to calculate $\\frac{\\partial y_i}{\\partial x_i}$. You should make sure each of the intermediary steps are all as simple as possible. \n",
    "\n",
    "After doing so, implement the simplified batch normalization backward pass in the function `batchnorm_backward_alt` and compare the two implementations by running the following. Your two implementations should compute nearly identical results, but the alternative implementation should be a bit faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx difference:  8.016783028634405e-13\n",
      "dgamma difference:  0.0\n",
      "dbeta difference:  0.0\n",
      "speedup: 2.00x\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D = 100, 500\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "out, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "t1 = time.time()\n",
    "dx1, dgamma1, dbeta1 = batchnorm_backward(dout, cache)\n",
    "t2 = time.time()\n",
    "dx2, dgamma2, dbeta2 = batchnorm_backward_alt(dout, cache)\n",
    "t3 = time.time()\n",
    "\n",
    "print('dx difference: ', rel_error(dx1, dx2))\n",
    "print('dgamma difference: ', rel_error(dgamma1, dgamma2))\n",
    "print('dbeta difference: ', rel_error(dbeta1, dbeta2))\n",
    "print('speedup: %.2fx' % ((t2 - t1) / (t3 - t2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fully Connected Nets with Batch Normalization\n",
    "Now that you have a working implementation for batch normalization, go back to your `FullyConnectedNet` in the file `cs231n/classifiers/fc_net.py`. Modify your implementation to add batch normalization.\n",
    "\n",
    "Concretely, when the `normalization` flag is set to `\"batchnorm\"` in the constructor, you should insert a batch normalization layer before each ReLU nonlinearity. The outputs from the last layer of the network should not be normalized. Once you are done, run the following to gradient-check your implementation.\n",
    "\n",
    "HINT: You might find it useful to define an additional helper layer similar to those in the file `cs231n/layer_utils.py`. If you decide to do so, do it in the file `cs231n/classifiers/fc_net.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.2611955101340957\n",
      "W1 relative error: 1.10e-04\n",
      "W2 relative error: 2.85e-06\n",
      "W3 relative error: 3.92e-10\n",
      "b1 relative error: 4.44e-08\n",
      "b2 relative error: 2.22e-08\n",
      "b3 relative error: 4.78e-11\n",
      "beta1 relative error: 7.33e-09\n",
      "beta2 relative error: 1.89e-09\n",
      "gamma1 relative error: 7.57e-09\n",
      "gamma2 relative error: 1.96e-09\n",
      "\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  6.996533220108303\n",
      "W1 relative error: 1.98e-06\n",
      "W2 relative error: 2.29e-06\n",
      "W3 relative error: 1.11e-08\n",
      "b1 relative error: 5.55e-09\n",
      "b2 relative error: 5.55e-09\n",
      "b3 relative error: 2.23e-10\n",
      "beta1 relative error: 6.65e-09\n",
      "beta2 relative error: 3.48e-09\n",
      "gamma1 relative error: 5.94e-09\n",
      "gamma2 relative error: 4.14e-09\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "# You should expect losses between 1e-4~1e-10 for W, \n",
    "# losses between 1e-08~1e-10 for b,\n",
    "# and losses between 1e-08~1e-09 for beta and gammas.\n",
    "for reg in [0, 3.14]:\n",
    "  print('Running check with reg = ', reg)\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64,\n",
    "                            normalization='batchnorm')\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print('Initial loss: ', loss)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n",
    "  if reg == 0: print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batchnorm for deep networks\n",
    "Run the following to train a six-layer network on a subset of 1000 training examples both with and without batch normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 200) loss: 2.340974\n",
      "(Epoch 0 / 10) train acc: 0.107000; val_acc: 0.115000\n",
      "(Epoch 1 / 10) train acc: 0.314000; val_acc: 0.266000\n",
      "(Iteration 21 / 200) loss: 2.039345\n",
      "(Epoch 2 / 10) train acc: 0.394000; val_acc: 0.286000\n",
      "(Iteration 41 / 200) loss: 2.045770\n",
      "(Epoch 3 / 10) train acc: 0.485000; val_acc: 0.318000\n",
      "(Iteration 61 / 200) loss: 1.768028\n",
      "(Epoch 4 / 10) train acc: 0.529000; val_acc: 0.317000\n",
      "(Iteration 81 / 200) loss: 1.236439\n",
      "(Epoch 5 / 10) train acc: 0.603000; val_acc: 0.331000\n",
      "(Iteration 101 / 200) loss: 1.358561\n",
      "(Epoch 6 / 10) train acc: 0.638000; val_acc: 0.319000\n",
      "(Iteration 121 / 200) loss: 1.021399\n",
      "(Epoch 7 / 10) train acc: 0.707000; val_acc: 0.317000\n",
      "(Iteration 141 / 200) loss: 1.136494\n",
      "(Epoch 8 / 10) train acc: 0.731000; val_acc: 0.315000\n",
      "(Iteration 161 / 200) loss: 0.808587\n",
      "(Epoch 9 / 10) train acc: 0.812000; val_acc: 0.317000\n",
      "(Iteration 181 / 200) loss: 0.692023\n",
      "(Epoch 10 / 10) train acc: 0.747000; val_acc: 0.300000\n",
      "(Iteration 1 / 200) loss: 2.302332\n",
      "(Epoch 0 / 10) train acc: 0.129000; val_acc: 0.131000\n",
      "(Epoch 1 / 10) train acc: 0.283000; val_acc: 0.250000\n",
      "(Iteration 21 / 200) loss: 2.041970\n",
      "(Epoch 2 / 10) train acc: 0.316000; val_acc: 0.277000\n",
      "(Iteration 41 / 200) loss: 1.900473\n",
      "(Epoch 3 / 10) train acc: 0.373000; val_acc: 0.282000\n",
      "(Iteration 61 / 200) loss: 1.713156\n",
      "(Epoch 4 / 10) train acc: 0.390000; val_acc: 0.310000\n",
      "(Iteration 81 / 200) loss: 1.662208\n",
      "(Epoch 5 / 10) train acc: 0.434000; val_acc: 0.300000\n",
      "(Iteration 101 / 200) loss: 1.696063\n",
      "(Epoch 6 / 10) train acc: 0.536000; val_acc: 0.346000\n",
      "(Iteration 121 / 200) loss: 1.550786\n",
      "(Epoch 7 / 10) train acc: 0.530000; val_acc: 0.310000\n",
      "(Iteration 141 / 200) loss: 1.436340\n",
      "(Epoch 8 / 10) train acc: 0.624000; val_acc: 0.348000\n",
      "(Iteration 161 / 200) loss: 0.998066\n",
      "(Epoch 9 / 10) train acc: 0.660000; val_acc: 0.332000\n",
      "(Iteration 181 / 200) loss: 0.943246\n",
      "(Epoch 10 / 10) train acc: 0.740000; val_acc: 0.345000\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [100, 100, 100, 100, 100]\n",
    "\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 2e-2\n",
    "bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization='batchnorm')\n",
    "model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "\n",
    "bn_solver = Solver(bn_model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True,print_every=20)\n",
    "bn_solver.train()\n",
    "\n",
    "solver = Solver(model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=20)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following to visualize the results from two networks trained above. You should find that using batch normalization helps the network to converge much faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f1df50a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_training_history(title, label, baseline, bn_solvers, plot_fn, bl_marker='.', bn_marker='.', labels=None):\n",
    "    \"\"\"utility function for plotting training history\"\"\"\n",
    "    plt.title(title)\n",
    "    plt.xlabel(label)\n",
    "    bn_plots = [plot_fn(bn_solver) for bn_solver in bn_solvers]   # 获取acc数据\n",
    "    bl_plot = plot_fn(baseline)\n",
    "    num_bn = len(bn_plots)\n",
    "    for i in range(num_bn):\n",
    "        label='with_norm'\n",
    "        if labels is not None:\n",
    "            label += str(labels[i])\n",
    "        plt.plot(bn_plots[i], bn_marker, label=label)\n",
    "    label='baseline'\n",
    "    if labels is not None:\n",
    "        label += str(labels[0])\n",
    "    plt.plot(bl_plot, bl_marker, label=label)\n",
    "    plt.legend(loc='lower center', ncol=num_bn+1) \n",
    "\n",
    "    \n",
    "plt.subplot(3, 1, 1)\n",
    "plot_training_history('Training loss','Iteration', solver, [bn_solver], \\\n",
    "                      lambda x: x.loss_history, bl_marker='o', bn_marker='o')\n",
    "plt.subplot(3, 1, 2)\n",
    "plot_training_history('Training accuracy','Epoch', solver, [bn_solver], \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-o', bn_marker='-o')\n",
    "plt.subplot(3, 1, 3)\n",
    "plot_training_history('Validation accuracy','Epoch', solver, [bn_solver], \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-o', bn_marker='-o')\n",
    "\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch normalization and initialization\n",
    "We will now run a small experiment to study the interaction of batch normalization and weight initialization.\n",
    "\n",
    "The first cell will train 8-layer networks both with and without batch normalization using different scales for weight initialization. The second layer will plot training accuracy, validation set accuracy, and training loss as a function of the weight initialization scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running weight scale 1 / 20\n",
      "Running weight scale 2 / 20\n",
      "Running weight scale 3 / 20\n",
      "Running weight scale 4 / 20\n",
      "Running weight scale 5 / 20\n",
      "Running weight scale 6 / 20\n",
      "Running weight scale 7 / 20\n",
      "Running weight scale 8 / 20\n",
      "Running weight scale 9 / 20\n",
      "Running weight scale 10 / 20\n",
      "Running weight scale 11 / 20\n",
      "Running weight scale 12 / 20\n",
      "Running weight scale 13 / 20\n",
      "Running weight scale 14 / 20\n",
      "Running weight scale 15 / 20\n",
      "Running weight scale 16 / 20\n",
      "Running weight scale 17 / 20\n",
      "Running weight scale 18 / 20\n",
      "Running weight scale 19 / 20\n",
      "Running weight scale 20 / 20\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [50, 50, 50, 50, 50, 50, 50]\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "bn_solvers_ws = {}\n",
    "solvers_ws = {}\n",
    "weight_scales = np.logspace(-4, 0, num=20)\n",
    "for i, weight_scale in enumerate(weight_scales):\n",
    "  print('Running weight scale %d / %d' % (i + 1, len(weight_scales)))\n",
    "  bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization='batchnorm')\n",
    "  model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "\n",
    "  bn_solver = Solver(bn_model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "  bn_solver.train()\n",
    "  bn_solvers_ws[weight_scale] = bn_solver\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "  solver.train()\n",
    "  solvers_ws[weight_scale] = solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f1f1d45c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot results of weight scale experiment\n",
    "best_train_accs, bn_best_train_accs = [], []\n",
    "best_val_accs, bn_best_val_accs = [], []\n",
    "final_train_loss, bn_final_train_loss = [], []\n",
    "\n",
    "for ws in weight_scales:\n",
    "  best_train_accs.append(max(solvers_ws[ws].train_acc_history))\n",
    "  bn_best_train_accs.append(max(bn_solvers_ws[ws].train_acc_history))\n",
    "  \n",
    "  best_val_accs.append(max(solvers_ws[ws].val_acc_history))\n",
    "  bn_best_val_accs.append(max(bn_solvers_ws[ws].val_acc_history))\n",
    "  \n",
    "  final_train_loss.append(np.mean(solvers_ws[ws].loss_history[-100:]))\n",
    "  bn_final_train_loss.append(np.mean(bn_solvers_ws[ws].loss_history[-100:]))\n",
    "  \n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Best val accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best val accuracy')\n",
    "plt.semilogx(weight_scales, best_val_accs, '-o', label='baseline')          # 对数坐标轴\n",
    "plt.semilogx(weight_scales, bn_best_val_accs, '-o', label='batchnorm')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Best train accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best training accuracy')\n",
    "plt.semilogx(weight_scales, best_train_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_train_accs, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Final training loss vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Final training loss')\n",
    "plt.semilogx(weight_scales, final_train_loss, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_final_train_loss, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "plt.gca().set_ylim(1.0, 3.5)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inline Question 1:\n",
    "Describe the results of this experiment. How does the scale of weight initialization affect models with/without batch normalization differently, and why?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answer:\n",
    "1. much more robust to bad initialization(even with a bad initialization can get good performance)\n",
    "2. much more robust to avoid overfitting（one some scale without bn the model the a bigger gap between val and train）\n",
    "3. prevent vanishing gradient and gradient explosion problem\n",
    "\n",
    "权重初始化对无batchnorm层的网络影响更加严重，即有BN层的网络对权重初始化的数值有一定的鲁棒性，因为BN层会对每层的数据做一定的变换，使数据的分布更加接近高斯分布，而在某些BN层需求弱的层，由于BN层中平移和缩放的存在使输入数据不会产生大幅的变换，所以有BN层的网络更加灵活，对权重初始化的敏感度不是特别强"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch normalization and batch size\n",
    "We will now run a small experiment to study the interaction of batch normalization and batch size.\n",
    "\n",
    "The first cell will train 6-layer networks both with and without batch normalization using different batch sizes. The second layer will plot training accuracy and validation set accuracy over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No normalization: batch size =  5\n",
      "Normalization: batch size =  5\n",
      "Normalization: batch size =  10\n",
      "Normalization: batch size =  50\n"
     ]
    }
   ],
   "source": [
    "def run_batchsize_experiments(normalization_mode):\n",
    "    np.random.seed(231)\n",
    "    # Try training a very deep net with batchnorm\n",
    "    hidden_dims = [100, 100, 100, 100, 100]\n",
    "    num_train = 1000\n",
    "    small_data = {\n",
    "      'X_train': data['X_train'][:num_train],\n",
    "      'y_train': data['y_train'][:num_train],\n",
    "      'X_val': data['X_val'],\n",
    "      'y_val': data['y_val'],\n",
    "    }\n",
    "    n_epochs=10\n",
    "    weight_scale = 2e-2\n",
    "    batch_sizes = [5,10,50]\n",
    "    lr = 10**(-3.5)\n",
    "    solver_bsize = batch_sizes[0]\n",
    "\n",
    "    print('No normalization: batch size = ',solver_bsize)\n",
    "    model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "    solver = Solver(model, small_data,\n",
    "                    num_epochs=n_epochs, batch_size=solver_bsize,\n",
    "                    update_rule='adam',\n",
    "                    optim_config={\n",
    "                      'learning_rate': lr,\n",
    "                    },\n",
    "                    verbose=False)\n",
    "    solver.train()\n",
    "    \n",
    "    bn_solvers = []\n",
    "    for i in range(len(batch_sizes)):\n",
    "        b_size=batch_sizes[i]\n",
    "        print('Normalization: batch size = ',b_size)\n",
    "        bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=normalization_mode)\n",
    "        bn_solver = Solver(bn_model, small_data,\n",
    "                        num_epochs=n_epochs, batch_size=b_size,\n",
    "                        update_rule='adam',\n",
    "                        optim_config={\n",
    "                          'learning_rate': lr,\n",
    "                        },\n",
    "                        verbose=False)\n",
    "        bn_solver.train()\n",
    "        bn_solvers.append(bn_solver)\n",
    "        \n",
    "    return bn_solvers, solver, batch_sizes\n",
    "\n",
    "batch_sizes = [5,10,50]\n",
    "bn_solvers_bsize, solver_bsize, batch_sizes = run_batchsize_experiments('batchnorm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f1f23ba20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 1, 1)\n",
    "plot_training_history('Training accuracy (Batch Normalization)','Epoch', solver_bsize, bn_solvers_bsize, \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "plt.subplot(2, 1, 2)\n",
    "plot_training_history('Validation accuracy (Batch Normalization)','Epoch', solver_bsize, bn_solvers_bsize, \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inline Question 2:\n",
    "Describe the results of this experiment. What does this imply about the relationship between batch normalization and batch size? Why is this relationship observed?\n",
    "\n",
    "## Answer:\n",
    "1. 从1图中可以看出在batch_size为5时使用BN后的精度会下降，是由于BN层有正则化的功能，会减小过拟合，所以训练精度下降，但在图2中同样batch_size的验证精度与无BN层的差不多\n",
    "2. 在增加batch_size后训练精度明显增加，但测试精度没有明显增加，只是略微提高，说明只增加batch_size无法有效的提升模型的泛化能力，batch_size对BN层的最终效果影响很大"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization\n",
    "Batch normalization has proved to be effective in making networks easier to train, but the dependency on batch size makes it less useful in complex networks which have a cap on the input batch size due to hardware limitations. \n",
    "\n",
    "Several alternatives to batch normalization have been proposed to mitigate this problem; one such technique is Layer Normalization [4]. Instead of normalizing over the batch, we normalize over the features. In other words, when using Layer Normalization, each feature vector corresponding to a single datapoint is normalized based on the sum of all terms within that feature vector.\n",
    "\n",
    "[4] [Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. \"Layer Normalization.\" stat 1050 (2016): 21.](https://arxiv.org/pdf/1607.06450.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inline Question 3:\n",
    "Which of these data preprocessing steps is analogous to batch normalization, and which is analogous to layer normalization?\n",
    "\n",
    "1. Scaling each image in the dataset, so that the RGB channels for each row of pixels within an image sums up to 1.\n",
    "2. Scaling each image in the dataset, so that the RGB channels for all pixels within an image sums up to 1.  \n",
    "3. Subtracting the mean image of the dataset from each image in the dataset.\n",
    "4. Setting all RGB values to either 0 or 1 depending on a given threshold.\n",
    "\n",
    "## Answer:\n",
    "batch normalization: 3\n",
    "\n",
    "layer normalization: 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization: Implementation\n",
    "\n",
    "Now you'll implement layer normalization. This step should be relatively straightforward, as conceptually the implementation is almost identical to that of batch normalization. One significant difference though is that for layer normalization, we do not keep track of the moving moments, and the testing phase is identical to the training phase, where the mean and variance are directly calculated per datapoint.\n",
    "\n",
    "Here's what you need to do:\n",
    "\n",
    "* In `cs231n/layers.py`, implement the forward pass for layer normalization in the function `layernorm_backward`. \n",
    "\n",
    "Run the cell below to check your results.\n",
    "* In `cs231n/layers.py`, implement the backward pass for layer normalization in the function `layernorm_backward`. \n",
    "\n",
    "Run the second cell below to check your results.\n",
    "* Modify `cs231n/classifiers/fc_net.py` to add layer normalization to the `FullyConnectedNet`. When the `normalization` flag is set to `\"layernorm\"` in the constructor, you should insert a layer normalization layer before each ReLU nonlinearity. \n",
    "\n",
    "Run the third cell below to run the batch size experiment on layer normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before layer normalization:\n",
      "  means:  [-59.06673243 -47.60782686 -43.31137368 -26.40991744]\n",
      "  stds:   [10.07429373 28.39478981 35.28360729  4.01831507]\n",
      "\n",
      "After layer normalization (gamma=1, beta=0)\n",
      "  means:  [-4.81096644e-16  0.00000000e+00  7.40148683e-17 -5.55111512e-16]\n",
      "  stds:   [0.99999995 0.99999999 1.         0.99999969]\n",
      "\n",
      "After layer normalization (gamma= [3. 3. 3.] , beta= [5. 5. 5.] )\n",
      "  means:  [5. 5. 5. 5.]\n",
      "  stds:   [2.99999985 2.99999998 2.99999999 2.99999907]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after layer normalization   \n",
    "\n",
    "# Simulate the forward pass for a two-layer network\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 =4, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print('Before layer normalization:')\n",
    "print_mean_std(a,axis=1)\n",
    "\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "# Means should be close to zero and stds close to one\n",
    "print('After layer normalization (gamma=1, beta=0)')\n",
    "a_norm, _ = layernorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=1)\n",
    "\n",
    "gamma = np.asarray([3.0,3.0,3.0])\n",
    "beta = np.asarray([5.0,5.0,5.0])\n",
    "# Now means should be close to beta and stds close to gamma\n",
    "print('After layer normalization (gamma=', gamma, ', beta=', beta, ')')\n",
    "a_norm, _ = layernorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  1.433615657860454e-09\n",
      "dgamma error:  4.519489546032799e-12\n",
      "dbeta error:  2.276445013433725e-12\n"
     ]
    }
   ],
   "source": [
    "# Gradient check batchnorm backward pass\n",
    "np.random.seed(231)\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "ln_param = {}\n",
    "fx = lambda x: layernorm_forward(x, gamma, beta, ln_param)[0]\n",
    "fg = lambda a: layernorm_forward(x, a, beta, ln_param)[0]\n",
    "fb = lambda b: layernorm_forward(x, gamma, b, ln_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n",
    "\n",
    "_, cache = layernorm_forward(x, gamma, beta, ln_param)\n",
    "dx, dgamma, dbeta = layernorm_backward(dout, cache)\n",
    "\n",
    "#You should expect to see relative errors between 1e-12 and 1e-8\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization and batch size\n",
    "\n",
    "We will now run the previous batch size experiment with layer normalization instead of batch normalization. Compared to the previous experiment, you should see a markedly smaller influence of batch size on the training history!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No normalization: batch size =  5\n",
      "Normalization: batch size =  5\n",
      "Normalization: batch size =  10\n",
      "Normalization: batch size =  50\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f1f82cd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ln_solvers_bsize, solver_bsize, batch_sizes = run_batchsize_experiments('layernorm')\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plot_training_history('Training accuracy (Layer Normalization)','Epoch', solver_bsize, ln_solvers_bsize, \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "plt.subplot(2, 1, 2)\n",
    "plot_training_history('Validation accuracy (Layer Normalization)','Epoch', solver_bsize, ln_solvers_bsize, \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图看出，batch_size的大小对使用了layernorm的网络影响不会太大"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inline Question 4:\n",
    "When is layer normalization likely to not work well, and why?\n",
    "\n",
    "1. Using it in a very deep network\n",
    "2. Having a very small dimension of features\n",
    "3. Having a high regularization term\n",
    "\n",
    "\n",
    "## Answer:\n",
    "\n",
    "3 使用过高的正则化参数会使网络欠拟合\n",
    "# ???????"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
